CC* Team: Oregon Big Data Research and Education Team

今天，大数据比以往任何时候都更渗透到生活的各个领域，环境、生物医学、地球、海洋、计算、物理、城市和社会科学，以及许多其他领域。日益强大的计算技术为研究人员开辟了一条途径，通过使用大型和不同种类的数据集以及通过复杂的模型和模拟来应对重大的全球挑战。该项目为包括研究学生在内的领域科学家提供了与这些先进的计算和统计方法方面的专家进行有效合作所需的专业知识和培训。该项目还为小型地区性大学的学生和教师提供培训和研究经验，其中包括服务于拉美裔和美洲原住民的机构。为了广泛分享最佳实践，它支持实践社区。该项目雇用了在数据集成、多模式数据分析和机器学习方面具有专业知识的研究和培训工作人员(促进者)。这三套相关的方法能够分析不同类型或来自不同来源的大型复杂数据集，这些数据集可能是也可能不是作为计划研究的一部分收集的。具体而言，俄勒冈州立大学、俄勒冈大学和波特兰州立大学成立了一个四人协调小组。跨学科、跨机构的团队将建立工具和管理实践，为该州的研究人员提供服务。由该项目推动的研究将有助于更好地了解地震、多样化的生态系统以及动植物的形态和功能。它支持开发更快的计算系统、更安全的能源系统和改善环境健康。这些应用领域带来的数据挑战也推动了高级数据分析和机器学习领域的新基础研究。该项目还培养了来自不同背景的新一代学生进入知识经济。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Today, more than ever before, big data pervade every area of the life, environmental, biomedical, earth, marine, computational, physical, urban, and social sciences, as well as numerous other domains. Increasingly powerful computing technologies have opened the pathway for researchers to address major global challenges through use of large and heterogeneous data sets and through complex models and simulations. This project provides domain scientists, including research students, with the expertise and training needed to collaborate effectively with specialists in these advanced computational and statistical methodologies. The project also provides training and research experiences for students and instructors from small regional colleges, including Hispanic-serving and Native-American-serving institutions. To widely share best practices, it supports a community of practice. The project employs research and training staff (facilitators) with expertise in data integration, multi-modal data analytics and machine learning. These three related sets of methods enable the analysis of large complex data sets of different types or from different sources, which may or may not have been collected as part of a planned studies. Specifically, a four-person facilitation team is established across Oregon State University, the University of Oregon, and Portland State University. The interdisciplinary, cross institutional team will establish the tools and managements practices to serve the researchers in the state. The research facilitated by this project will lead to better understanding of earthquakes, diverse ecosystems, and plant and animal form and function. It supports development of faster computing systems, more secure energy systems, and improved environmental health. The data challenges posed by these application areas also motivate new foundational research in advanced data analytics and machine learning. The project also prepares a new generation of students from diverse backgrounds to enter the knowledge economy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.